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作 者:Edward V.Camp Daniel D.Johnson Matthew D.Taylor
机构地区:[1]Fisheries and Aquatic Sciences,School of Forest,Fisheries,and Geomatics Sciences,University of Florida,Gainesville,32611,FL,USA [2]Port Stephens Fisheries Institute,New South Wales Department of Primary Industries,Taylors Beach Rd,Taylors Beach,New South Wales,2315,Australia [3]School of Environmental and Life Sciences,University of Newcastle,New South Wales,2308,Australia
出 处:《Aquaculture and Fisheries》2023年第2期190-201,共12页渔业学报(英文)
基 金:supported by the Fisheries Research and Development Corporation on behalf of the Australian Government through a grant to MDT and DDJ(2016/020)。
摘 要:Spatial management of fishing effort can be used to avoid catching undesirable size classes for target species,and improve yield-per-recruit for the exploited stock.Adaptive closure management has been proposed as a means to more effectively utilise spatial management,however these management provisions often lack quantitative evaluation which constrains the information available to inform decisions.We demonstrate the use of a spatially and size structured population dynamics model to evaluate the potential impact of spatial management on a multijurisdictional fishery for a highly migratory species(eastern king prawn,Penaeus[Melicertus]plebejus).Under current conditions in the fishery,the overall effect of closures on harvest was estimated to be comparatively minor,regardless of assumptions about how effort or fisher behavior are affected by spatial management.Alternative assumptions about the movement patterns of eastern king prawn had little influence on the impact of closures on overall harvest.However,when effort was increased to historic levels similar to those observed when the closures were implemented,a much greater impact on overall harvest was observed.The approach taken and simulation outcomes are discussed in the context of spatial management for both eastern king prawn,and penaeid fisheries more broadly.
关 键 词:PENAEIDAE TRAWL Management strategy evaluation CLOSURES Adaptive management
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